Liu et al., 2023 - Google Patents
Deep reinforcement learning for real-time economic energy management of microgrid system considering uncertaintiesLiu et al., 2023
View HTML- Document ID
- 4032256929061609900
- Author
- Liu D
- Zang C
- Zeng P
- Li W
- Wang X
- Liu Y
- Xu S
- Publication year
- Publication venue
- Frontiers in Energy Research
External Links
Snippet
The electric power grid is changing from a traditional power system to a modern, smart, and integrated power system. Microgrids (MGs) play a vital role in combining distributed renewable energy resources (RESs) with traditional electric power systems. Intermittency …
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B15/00—Systems controlled by a computer
- G05B15/02—Systems controlled by a computer electric
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S40/00—Communication or information technology specific aspects supporting electrical power generation, transmission, distribution or end-user application management
- Y04S40/20—Information technology specific aspects
- Y04S40/22—Computer aided design [CAD]; Simulation; Modelling
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Hossain et al. | Energy management of community energy storage in grid-connected microgrid under uncertain real-time prices | |
Raya-Armenta et al. | Energy management system optimization in islanded microgrids: An overview and future trends | |
Hua et al. | Optimal energy management strategies for energy Internet via deep reinforcement learning approach | |
Luo et al. | Short‐term operational planning framework for virtual power plants with high renewable penetrations | |
Guo et al. | Optimal energy management of multi-microgrids connected to distribution system based on deep reinforcement learning | |
Harrold et al. | Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning | |
Kuznetsova et al. | Reinforcement learning for microgrid energy management | |
Mbuwir et al. | Reinforcement learning for control of flexibility providers in a residential microgrid | |
Joshi et al. | Survey on AI and machine learning techniques for microgrid energy management systems | |
Bui et al. | Real-time operation of distribution network: A deep reinforcement learning-based reconfiguration approach | |
CN116207739B (en) | Optimal scheduling method and device for power distribution network, computer equipment and storage medium | |
Mohammadi et al. | Stochastic scenario‐based model and investigating size of energy storages for PEM‐fuel cell unit commitment of micro‐grid considering profitable strategies | |
Liu et al. | Deep reinforcement learning for real-time economic energy management of microgrid system considering uncertainties | |
Ruelens et al. | Demand side management of electric vehicles with uncertainty on arrival and departure times | |
CN116451880B (en) | Distributed energy optimization scheduling method and device based on hybrid learning | |
Xiao et al. | Multi-period data driven control strategy for real-time management of energy storages in virtual power plants integrated with power grid | |
Zhang et al. | Economical operation strategy of an integrated energy system with wind power and power to gas technology–a DRL‐based approach | |
Zhang et al. | Physical-model-free intelligent energy management for a grid-connected hybrid wind-microturbine-PV-EV energy system via deep reinforcement learning approach | |
El Bourakadi et al. | Multi-agent system based sequential energy management strategy for Micro-Grid using optimal weighted regularized extreme learning machine and decision tree | |
Zhu et al. | Real‐time stochastic operation strategy of a microgrid using approximate dynamic programming‐based spatiotemporal decomposition approach | |
Domínguez-Barbero et al. | Twin-delayed deep deterministic policy gradient algorithm for the energy management of microgrids | |
Darshi et al. | Decentralized energy management system for smart microgrids using reinforcement learning | |
Tan et al. | Low‐carbon economic dispatch of the combined heat and power‐virtual power plants: A improved deep reinforcement learning‐based approach | |
Liu et al. | Deep reinforcement learning for stochastic dynamic microgrid energy management | |
Chen et al. | Reinforcement learning based two‐timescale energy management for energy hub |